During inference on Aircraft and CUB (using bounding-box cropped images as input), images are resized into 92\(\times \) 92 then center-cropped into 84\(\times \) 84i.e., torchvision.transforms.Resize([92,92]) followed by torchvision.transforms.CenterCrop(84). For mini-ImageNet, tiered-ImageNet (data from DeepEMD [1]}'s implementation) and CUB (using raw images as input), images are resized such that the smaller edges will match the length of 92i.e., torchvision.transforms.Resize(92) then center-cropped into 84\(\times \) 84. This prevents distortion artifacts at test time. For meta-iNat, tiered meta-iNat, and tiered-ImageNet using the original release by Ren  [2]}, images are already at size 84\(\times \) 84 and are therefore fed into models directly.
